Fusion-based 4D reconstruction.
DynamicFusion [1]} is the pioneering work for reconstructing non-rigid scenes from depth videos in real-time. It fuses multiple depth frames into a canonical frame to recover the observable surface and adopt a dense volumetric 6D motion field for modeling motion. Another early work [2]} reconstructs dynamic objects without explicit motion fields. Improvements of DynamicFusion leverage more RGB information [3]}, introduce extra regularization [4]}, [5]}, develop efficient representations [6]}, and predict additional surface properties [7]}. Importantly, different from the proposed direction, these works only recover the observable surfaces rather then the complete shape. Moreover, these works often fail to handle fast motion and changing occlusion. Note, these models often have no trainable parameters and are more geometry-based.
